A. Booth, P. Christoffersen, A. Pretorius, J. Chapman, B. Hubbard, Emma C. Smith, S. D. de Ridder, A. Nowacki, B. Lipovsky, M. Denolle
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Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach
Abstract Distributed Acoustic Sensing (DAS) is increasingly recognised as a valuable tool for glaciological seismic applications, although analysing the large data volumes generated in acquisitions poses computational challenges. We show the potential of active-source DAS to image and characterise subglacial sediment beneath a fast-flowing Greenlandic outlet glacier, estimating the thickness of sediment layers to be 20–30 m. However, the lack of subglacial velocity constraint limits the accuracy of this estimate. Constraint could be provided by analysing cryoseismic events in a counterpart 3-day record of passive seismicity through, for example, seismic tomography, but locating them within the 9 TB data volume is computationally inefficient. We describe experiments with data compression using the frequency-wavenumber (f-k) transform ahead of training a convolutional neural network, that provides a ~300-fold improvement in efficiency. In combining active and passive-source and our machine learning framework, the potential of large DAS datasets could be unlocked for a range of future applications.
期刊介绍:
Annals of Glaciology publishes original scientific articles and letters in selected aspects of glaciology-the study of ice. Each issue of the Annals is thematic, focussing on a specific subject. The Council of the International Glaciological Society welcomes proposals for thematic issues from the glaciological community. Once a theme is approved, the Council appoints an Associate Chief Editor and a team of Scientific Editors to handle the submission, peer review and publication of papers.